We first ablate the various components of gsf and validate the design choices. We choose BNInception as the backbone model due to its reduced complexity. The results are reported in Tab. REF . By removing the channel fusion, gsf converges to gsm [1]}, our prior work on top of which gsf is built upon, and underperforms by \(0.85\%\) . This validates the importance of the learned channel fusion. We then fix the spatial gating to a fixed value of 1 which results in an accuracy of \(45.57\%\) . This is similar to tsm [2]} where temporal shifting is applied to all the channels. With a fixed gating of -1, the networks performs differencing operation between the adjacent frames and results in \(45.89\%\) . This is a special case of tdn [3]} without the image subnetwork. Finally, we fix the gating to 0 which converges the model to the baseline,  tsn, without any temporal interaction between the frame level features, and drops the accuracy by 30%.
<TABLE>